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- Document type:
- Forschungsdaten
- Publication date:
- 04.06.2024
- Responsible:
- Vega-Torres, Miguel-A.
- Authors:
- Vega-Torres, Miguel-A. ; Braun, Alexander; Borrmann, André
- Author affiliation:
- TUM
- Publisher:
- TUM
- Title:
- ConSLAM BIM and GT Poses
- Identifier:
- doi:10.14459/2024mp1743877
- End date of data production:
- 15.03.2024
- Subject area:
- BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; ELT Elektrotechnik; TEC Technik, Ingenieurwissenschaften (allgemein); VER Technik der Verkehrsmittel
- Resource type:
- Experimente und Beobachtungen / experiments and observations; Statistik und Referenzdaten / statistics and reference data
- Other resource types:
- Manual creation for the model and automatic for the poses
- Data type:
- mehrdimensionale Visualisierungen oder Modelle / models
- Other data type:
- Industry Fundation Classes (IFC) and Revit model, and .TUM files with poses information
- Description:
- The ConSLAM BIM and GT Poses comprehends the 3D building information model (in IFC and Revit formats), manually elaborated based on the terrestrial laser scanner of the sequence 2 of ConSLAM, and the refined grounth truth (GT) poses (in TUM format) of the sessions 2, 3, 4 and 5 of the open-access ConSLAM dataset. This dataset can be found here: https://github.com/mac137/ConSLAM
- Method of data assessment:
- 3D geometric and semantic BIM model generated with Autodesk revit and the 6-degrees-of-freedom time-stammed poses resulted from our proposed SLAM2REF method: https://github.com/MigVega/SLAM2REF
- Key words:
- LiDAR; Multi-Session SLAM; Pose-Graph Optimization; Loop Closure; Long-term Mapping; Change Detection; BIM Update; 3D Indoor Localization and Mapping
- Technical remarks:
- View and download (71 MB total, 7 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1743877):
rsync rsync://m1743877@dataserv.ub.tum.de/m1743877/ - Language:
- en
- Rights:
- by-nc, http://creativecommons.org/licenses/by-nc/4.0
- Horizon 2020:
- This research is part of the INTREPID project funded by EU's Horizon 2020 program (Grant agreement ID: 883345)
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